The present invention relates to optical character recognition (OCR). More specifically the present invention relates to adaptive OCR on a document with distorted characters.
OCR technology has been used for decades for converting scanned images of documents to indexable text. The accuracy of commercially available OCR engines has constantly improved, to the extent that the OCR problem has been sometimes regarded as solved. However, although the accuracy of such OCR engines may be sufficient for occasional home or business use, the accuracy may not be sufficient for heavier use. For example, a typical mean error rate at the word level ranging from about 1% to 10% may not be adequate for many information retrieval applications. For example, even a relatively low error rate may not provide sufficient accuracy to efficiently convert an entire library, or other large collection of books.
OCR typically begins with segmenting a scanned or otherwise acquired image of a text into separate characters. Once segmented, an OCR system or method may apply one or more techniques to identify a segmented character. The system or method may then incorporate the identified character into indexable text.
For example, OCR technology may include template matching, in which a segmented character is compared against templates of various characters. A template matching algorithm may assign a score that rates the similarity between the segmented character and the template. For example, assigning a score may include application of a classification algorithm such as a neural network or a support vector machine (SVM).
As another example, OCR technology may be feature based, extracting one or more features from a segmented character. A feature based OCR algorithm may then employ a classification algorithm, such as one of the classification algorithms mentioned above, to compare the extracted features against features characterizing a previously identified character.